import pandas as pd
import numpy as np
url = url = "https://docs.google.com/spreadsheets/d/1mS6khEB6m8cPNenNvY9Tg6bJ6YkmcvCI/export?format=csv&gid=1327379612"
df = pd.read_csv(url)
df.head()
| TERM | SUBJECT | NBR | COURSE NAME | SECTION | PROF | TOTAL | A+ | A | A- | ... | B | B- | C+ | C | C- | D | F | W | INC/NA | AVG GPA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | S2023 | AACS | 107 | Immigrant Communities Queens | 1 | KHANDELWAL, M | 13 | 0 | 1 | 5 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3.643 |
| 1 | S2023 | ACCT | 100 | Fin & Mgr Acct | 1 | HO, V | 20 | 0 | 5 | 3 | ... | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3.382 |
| 2 | S2023 | ACCT | 101 | Intro Thry & Prac of Acct I | 11 | CHAN, J | 29 | 0 | 13 | 1 | ... | 5 | 0 | 2 | 0 | 0 | 1 | 0 | 4 | 0 | 3.448 |
| 3 | S2023 | ACCT | 101 | Intro Thry & Prac of Acct I | 8 | FEISULLIN, A | 40 | 5 | 2 | 5 | ... | 3 | 3 | 8 | 2 | 0 | 0 | 2 | 5 | 1 | 2.918 |
| 4 | S2023 | ACCT | 101 | Intro Thry & Prac of Acct I | 9 | SUN, F | 40 | 0 | 2 | 10 | ... | 5 | 0 | 1 | 1 | 3 | 5 | 2 | 6 | 1 | 2.634 |
5 rows × 21 columns
analysis_df = df.drop(columns = ['TERM','SECTION','INC/NA'])
analysis_df
| SUBJECT | NBR | COURSE NAME | PROF | TOTAL | A+ | A | A- | B+ | B | B- | C+ | C | C- | D | F | W | AVG GPA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AACS | 107 | Immigrant Communities Queens | KHANDELWAL, M | 13 | 0 | 1 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3.643 |
| 1 | ACCT | 100 | Fin & Mgr Acct | HO, V | 20 | 0 | 5 | 3 | 1 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | 3.382 |
| 2 | ACCT | 101 | Intro Thry & Prac of Acct I | CHAN, J | 29 | 0 | 13 | 1 | 3 | 5 | 0 | 2 | 0 | 0 | 1 | 0 | 4 | 3.448 |
| 3 | ACCT | 101 | Intro Thry & Prac of Acct I | FEISULLIN, A | 40 | 5 | 2 | 5 | 4 | 3 | 3 | 8 | 2 | 0 | 0 | 2 | 5 | 2.918 |
| 4 | ACCT | 101 | Intro Thry & Prac of Acct I | SUN, F | 40 | 0 | 2 | 10 | 3 | 5 | 0 | 1 | 1 | 3 | 5 | 2 | 6 | 2.634 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2403 | URBST | 371W | VT: Service Learning Project | GOLDFISCHER, E | 23 | 4 | 7 | 3 | 2 | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 3.536 |
| 2404 | URBST | 373W | Spec. Problems-Environ Studies | CONSTANTINIDES, C | 20 | 7 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4.000 |
| 2405 | WGS | 101W | Intro Women & Gender Studies | GIARDINA, C | 25 | 0 | 6 | 7 | 4 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 3.536 |
| 2406 | WGS | 101W | Intro Women & Gender Studies | GIARDINA, C | 25 | 0 | 5 | 5 | 5 | 2 | 1 | 1 | 0 | 1 | 1 | 1 | 3 | 3.123 |
| 2407 | WGS | 201W | Theories of Feminism | CRANDALL, E | 15 | 0 | 3 | 4 | 0 | 0 | 1 | 2 | 1 | 1 | 0 | 0 | 3 | 3.150 |
2408 rows × 18 columns
unique(): This function returns an array of all unique values in the order that they appear in the original DataFrame or Series. It's useful when you want to see or use the actual unique values (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.unique.html)
nunique(): This function returns an integer that represents the number of unique values. It's useful when you just want to know how many unique values exist, rather than what those unique values are (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.nunique.html)
def gpa_letter_converter(gpa):
letter_grades = {
"A": 4.0,
"A-": (3.7, 3.8, 3.9),
"B+": (3.3, 3.4, 3.5, 3.6),
"B": (3.0, 3.1, 3.2),
"B-": (2.7, 2.8, 2.9),
"C+": (2.3, 2.4, 2.5, 2.6),
"C": (2.0, 2.1, 2.2),
"C-": (1.7, 1.8, 1.9),
"D": (1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6),
"F": (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)
}
for letter_grade, number_grade in letter_grades.items():
if isinstance(number_grade, float) and gpa == number_grade:
return letter_grade
elif isinstance(number_grade, tuple) and gpa in number_grade:
return letter_grade
return None
def calculate_average_gpas(df):
# Prepare a list to store the results
results = []
# GPA equivalents for each letter grade
letter_grades_to_gpa = {
"A+": 4.0,
"A": 4.0,
"A-": 3.7,
"B+": 3.3,
"B": 3.0,
"B-": 2.7,
"C+": 2.3,
"C": 2.0,
"C-": 1.7,
"D": 1.0,
"F": 0.0
}
# Loop over all unique course numbers
for class_nbr in df["NBR"].unique():
# Filter the DataFrame for the current course number
df_nbr = df[df["NBR"] == class_nbr]
# Loop over all unique professors for the current course number
for prof in df_nbr["PROF"].unique():
# Filter the DataFrame for the current professor
df_prof = df_nbr[df_nbr["PROF"] == prof]
# Calculate the average GPA for the current professor and course number
avg_gpa_prof = round(df_prof["AVG GPA"].mean(), 1)
# Convert the individual grade counts to GPA equivalents and calculate the standard deviation
gpa_distributions = []
for grade_letter, gpa in letter_grades_to_gpa.items():
gpa_distributions.extend([gpa] * df_prof[grade_letter].sum())
std_dev_gpa_prof = round(np.std(gpa_distributions), 2)
# Append the result to the list
results.append({
"CLASS NUMBER": class_nbr,
"PROF": prof,
"AVG GPA PROF": avg_gpa_prof,
"AVG GPA PROF LETTER": gpa_letter_converter(avg_gpa_prof),
"STD DEV GPA PROF": std_dev_gpa_prof
})
# If there is more than one professor for this course, calculate the average GPA for the current course number, regardless of the professor
if df_nbr["PROF"].nunique() > 1:
avg_gpa_nbr = round(df_nbr["AVG GPA"].mean(), 1)
# Convert the individual grade counts to GPA equivalents and calculate the standard deviation
gpa_distributions = []
for grade_letter, gpa in letter_grades_to_gpa.items():
gpa_distributions.extend([gpa] * df_nbr[grade_letter].sum())
std_dev_gpa_nbr = round(np.std(gpa_distributions), 2)
# Append the result to the list
results.append({
"CLASS NUMBER": class_nbr,
"PROF": "All Professors",
"AVG GPA PROF": avg_gpa_nbr,
"AVG GPA PROF LETTER": gpa_letter_converter(avg_gpa_nbr),
"STD DEV GPA PROF": std_dev_gpa_nbr
})
# Convert the list of results to a DataFrame
df_results = pd.DataFrame(results)
# Find the hardest class based on average GPA
hardest_class = df_results[df_results["PROF"] == "All Professors"].sort_values("AVG GPA PROF").iloc[0]
# Calculate the average GPA for the entire subject
avg_gpa_subject = round(df["AVG GPA"].mean(), 1)
print(f"Average GPA for this entire subject in Spring 2023 is: {avg_gpa_subject}, which is a {gpa_letter_converter(avg_gpa_subject)}")
print(f"The hardest class based on average GPA in Spring 2023 is class number: {hardest_class['CLASS NUMBER']} with an average GPA of {hardest_class['AVG GPA PROF']}, which is a {hardest_class['AVG GPA PROF LETTER']}")
print("\nStandard deviation tells us about the spread of the grades that students received in each class. A higher standard deviation indicates a wider range of grades, while a lower standard deviation indicates that grades were more closely clustered around the average.")
return df_results
def calculate_teacher_gpas(df):
# Prepare a list to store the results
results = []
# GPA equivalents for each letter grade
letter_grades_to_gpa = {
"A+": 4.0,
"A": 4.0,
"A-": 3.7,
"B+": 3.3,
"B": 3.0,
"B-": 2.7,
"C+": 2.3,
"C": 2.0,
"C-": 1.7,
"D": 1.0,
"F": 0.0
}
# Loop over all unique professors
for prof in sorted(df["PROF"].unique()):
# Filter the DataFrame for the current professor
df_prof = df[df["PROF"] == prof]
# Calculate the average GPA for the current professor
avg_gpa_prof = round(df_prof["AVG GPA"].mean(), 1)
# Convert the individual grade counts to GPA equivalents and calculate the standard deviation
gpa_distributions = []
for grade_letter, gpa in letter_grades_to_gpa.items():
gpa_distributions.extend([gpa] * df_prof[grade_letter].sum())
std_dev_gpa_prof = round(np.std(gpa_distributions), 1)
# Calculate the percentage of students who withdrew for the current professor
withdraw_percentage = df_prof["W"].sum() / df_prof["TOTAL"].sum()
# Append the result to the list
results.append({
"PROF": prof,
"AVG GPA PROF": avg_gpa_prof,
"AVG GPA PROF LETTER": gpa_letter_converter(avg_gpa_prof),
"STD DEV GPA PROF": std_dev_gpa_prof,
"NUM OF CLASSES": len(df_prof),
"WITHDRAW PERCENTAGE": round(withdraw_percentage * 100, 1)
})
# Convert the list of results to a DataFrame
df_results = pd.DataFrame(results)
# Calculate the average GPA for the entire subject
avg_gpa_subject = round(df["AVG GPA"].mean(), 1)
# Calculate the average standard deviation for the entire subject
gpa_distributions = []
for grade_letter, gpa in letter_grades_to_gpa.items():
gpa_distributions.extend([gpa] * df[grade_letter].sum())
avg_std_dev_subject = round(np.std(gpa_distributions), 1)
# Calculate the average withdrawal percentage for the entire subject
withdraw_percentage_subject = round((df["W"].sum() / df["TOTAL"].sum()) * 100, 2)
# Find the professors who teach more than one class, have a GPA that is the same or higher than the subject average GPA,
# and for whom less than 40% of students withdrew
best_profs = df_results[(df_results["NUM OF CLASSES"] > 1) & (df_results["AVG GPA PROF"] >= avg_gpa_subject) & (df_results["WITHDRAW PERCENTAGE"] <= withdraw_percentage_subject)]
print(f"Professors who teach more than one class, have a GPA that is the same or higher than the subject average GPA ({avg_gpa_subject}), and have a withdrawal percentage that is less or equal than the subject average withdrawal percentage ({withdraw_percentage_subject}%):\n")
print(best_profs, "\n")
print("Note: This ignores rate my professor ratings. This is soley based on GPA and it doesn't consider how much students actually learn from these teachers!")
print(f"Average standard deviation of GPA for all teachers in this subject in Spring 2023 is: {avg_std_dev_subject}")
return df_results
analysis_df = analysis_df.dropna(subset = "PROF")
analysis_df = analysis_df[analysis_df["AVG GPA"] != 0]
math_df = analysis_df[analysis_df["SUBJECT"] == "MATH"]
pd.set_option('display.max_rows', None)
math_df
| SUBJECT | NBR | COURSE NAME | PROF | TOTAL | A+ | A | A- | B+ | B | B- | C+ | C | C- | D | F | W | AVG GPA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1670 | MATH | 110 | Math Literacy | ALI, M | 32 | 13 | 3 | 3 | 1 | 3 | 1 | 1 | 1 | 0 | 0 | 0 | 4 | 3.631 |
| 1671 | MATH | 110 | Math Literacy | ALI, M | 31 | 10 | 3 | 0 | 3 | 2 | 1 | 3 | 1 | 1 | 0 | 0 | 6 | 3.383 |
| 1672 | MATH | 110 | Math Literacy | LASKER, M | 32 | 4 | 4 | 2 | 4 | 1 | 6 | 2 | 0 | 1 | 2 | 2 | 4 | 2.861 |
| 1673 | MATH | 110 | Math Literacy | GILLMAN, P | 40 | 2 | 0 | 3 | 1 | 1 | 0 | 1 | 3 | 1 | 4 | 10 | 13 | 1.515 |
| 1674 | MATH | 110 | Math Literacy | GILLMAN, P | 40 | 0 | 1 | 0 | 0 | 2 | 1 | 0 | 2 | 3 | 9 | 11 | 9 | 1.062 |
| 1675 | MATH | 114 | Elementary Prob&Stat | CHOW, J | 11 | 3 | 0 | 1 | 0 | 1 | 1 | 2 | 0 | 0 | 1 | 0 | 2 | 3.000 |
| 1676 | MATH | 115 | College Algebra for Precalc | ALI, M | 28 | 4 | 7 | 5 | 1 | 0 | 1 | 1 | 2 | 1 | 1 | 0 | 5 | 3.370 |
| 1677 | MATH | 115 | College Algebra for Precalc | UDDIN, J | 23 | 2 | 5 | 1 | 3 | 2 | 1 | 0 | 3 | 1 | 0 | 3 | 2 | 2.762 |
| 1678 | MATH | 115 | College Algebra for Precalc | NAKAYAMA, A | 35 | 8 | 1 | 1 | 4 | 2 | 1 | 1 | 1 | 2 | 3 | 4 | 6 | 2.582 |
| 1679 | MATH | 115 | College Algebra for Precalc | JOANIDHI, Z | 20 | 0 | 2 | 3 | 0 | 2 | 2 | 0 | 1 | 2 | 0 | 2 | 5 | 2.564 |
| 1680 | MATH | 115 | College Algebra for Precalc | OSTROWSKY, A | 27 | 2 | 4 | 0 | 2 | 1 | 1 | 1 | 1 | 2 | 3 | 2 | 8 | 2.474 |
| 1681 | MATH | 115 | College Algebra for Precalc | TOBAR, J | 35 | 0 | 2 | 3 | 2 | 3 | 1 | 0 | 2 | 3 | 6 | 1 | 9 | 2.283 |
| 1682 | MATH | 115 | College Algebra for Precalc | XU, R | 20 | 2 | 1 | 0 | 0 | 2 | 1 | 4 | 2 | 0 | 4 | 2 | 1 | 2.106 |
| 1683 | MATH | 115 | College Algebra for Precalc | LEHMAN, S | 17 | 0 | 2 | 1 | 0 | 0 | 2 | 0 | 2 | 1 | 2 | 2 | 5 | 2.067 |
| 1684 | MATH | 115 | College Algebra for Precalc | KALRA, P | 16 | 0 | 0 | 3 | 1 | 0 | 1 | 1 | 2 | 1 | 1 | 3 | 3 | 2.008 |
| 1685 | MATH | 115 | College Algebra for Precalc | BERGER, K | 34 | 3 | 2 | 1 | 1 | 0 | 0 | 2 | 2 | 0 | 6 | 6 | 11 | 1.809 |
| 1686 | MATH | 115 | College Algebra for Precalc | BOTEJU, W | 35 | 0 | 1 | 4 | 1 | 0 | 0 | 2 | 2 | 2 | 4 | 7 | 9 | 1.657 |
| 1687 | MATH | 115 | College Algebra for Precalc | LEHMAN, S | 18 | 0 | 0 | 0 | 0 | 1 | 2 | 2 | 1 | 0 | 4 | 2 | 4 | 1.583 |
| 1688 | MATH | 115 | College Algebra for Precalc | PORCHETTA, E | 30 | 0 | 3 | 0 | 0 | 2 | 1 | 1 | 1 | 3 | 0 | 9 | 9 | 1.505 |
| 1689 | MATH | 115 | College Algebra for Precalc | TOBAR, J | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 1 | 8 | 1.500 |
| 1690 | MATH | 115 | College Algebra for Precalc | BOTEJU, W | 35 | 1 | 1 | 1 | 2 | 1 | 2 | 3 | 0 | 0 | 3 | 13 | 8 | 1.356 |
| 1691 | MATH | 115 | College Algebra for Precalc | WOLF-SONKIN, V | 27 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 2 | 1 | 1 | 6 | 12 | 1.315 |
| 1692 | MATH | 115 | College Algebra for Precalc | SHEN, T | 28 | 0 | 1 | 0 | 0 | 0 | 2 | 2 | 2 | 1 | 2 | 9 | 8 | 1.142 |
| 1693 | MATH | 115 | College Algebra for Precalc | WOLF-SONKIN, V | 30 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 9 | 15 | 0.769 |
| 1694 | MATH | 119 | Math for Elem School Teachers | GREENMAN, J | 25 | 5 | 5 | 0 | 4 | 3 | 0 | 2 | 2 | 1 | 0 | 1 | 2 | 3.152 |
| 1695 | MATH | 119 | Math for Elem School Teachers | RICCARDO, M | 30 | 6 | 3 | 5 | 2 | 5 | 0 | 0 | 1 | 1 | 2 | 1 | 4 | 3.146 |
| 1696 | MATH | 119 | Math for Elem School Teachers | KLOSIN, K | 27 | 1 | 7 | 4 | 3 | 3 | 0 | 0 | 3 | 2 | 3 | 1 | 0 | 2.893 |
| 1697 | MATH | 119 | Math for Elem School Teachers | CHOW, J | 24 | 1 | 0 | 5 | 1 | 6 | 2 | 4 | 1 | 0 | 0 | 2 | 2 | 2.745 |
| 1698 | MATH | 119 | Math for Elem School Teachers | BERGER, K | 28 | 2 | 2 | 4 | 2 | 5 | 2 | 2 | 0 | 1 | 1 | 3 | 4 | 2.713 |
| 1699 | MATH | 119 | Math for Elem School Teachers | CHOW, J | 20 | 0 | 5 | 4 | 1 | 0 | 2 | 0 | 0 | 0 | 1 | 4 | 3 | 2.618 |
| 1700 | MATH | 119 | Math for Elem School Teachers | LIN, Y | 22 | 2 | 4 | 1 | 0 | 2 | 1 | 2 | 1 | 0 | 4 | 1 | 4 | 2.611 |
| 1701 | MATH | 119 | Math for Elem School Teachers | LI, H | 30 | 1 | 2 | 1 | 1 | 2 | 2 | 2 | 1 | 0 | 6 | 5 | 7 | 1.870 |
| 1702 | MATH | 120 | Discrete Math Comp Sci | TERILLA, J | 25 | 1 | 6 | 4 | 1 | 4 | 1 | 0 | 4 | 1 | 0 | 1 | 2 | 3.065 |
| 1703 | MATH | 120 | Discrete Math Comp Sci | GOLDMAN, S | 28 | 0 | 5 | 3 | 6 | 3 | 3 | 0 | 3 | 1 | 0 | 1 | 3 | 3.028 |
| 1704 | MATH | 120 | Discrete Math Comp Sci | GONZALEZ, R | 30 | 3 | 3 | 1 | 2 | 1 | 2 | 2 | 5 | 2 | 1 | 0 | 7 | 2.805 |
| 1705 | MATH | 120 | Discrete Math Comp Sci | LEE, H | 25 | 2 | 0 | 1 | 2 | 2 | 7 | 2 | 2 | 3 | 2 | 0 | 2 | 2.561 |
| 1706 | MATH | 120 | Discrete Math Comp Sci | LEE, D | 30 | 0 | 0 | 4 | 7 | 2 | 0 | 1 | 1 | 4 | 3 | 1 | 7 | 2.522 |
| 1707 | MATH | 120 | Discrete Math Comp Sci | TOBAR, J | 35 | 0 | 4 | 2 | 4 | 1 | 0 | 1 | 2 | 1 | 2 | 3 | 15 | 2.480 |
| 1708 | MATH | 120 | Discrete Math Comp Sci | GONZALEZ, R | 35 | 3 | 2 | 0 | 5 | 2 | 0 | 3 | 3 | 3 | 2 | 3 | 9 | 2.404 |
| 1709 | MATH | 120 | Discrete Math Comp Sci | BERGER, K | 35 | 3 | 0 | 4 | 2 | 2 | 5 | 2 | 2 | 1 | 4 | 3 | 7 | 2.400 |
| 1710 | MATH | 120 | Discrete Math Comp Sci | SABITOVA, M | 29 | 1 | 2 | 0 | 0 | 2 | 0 | 0 | 8 | 0 | 2 | 3 | 11 | 2.000 |
| 1711 | MATH | 120 | Discrete Math Comp Sci | GONZALEZ, R | 30 | 1 | 1 | 0 | 0 | 1 | 3 | 1 | 1 | 3 | 4 | 3 | 12 | 1.806 |
| 1712 | MATH | 120 | Discrete Math Comp Sci | SHEN, T | 28 | 1 | 0 | 2 | 1 | 0 | 1 | 2 | 1 | 5 | 5 | 3 | 7 | 1.786 |
| 1713 | MATH | 120 | Discrete Math Comp Sci | BOTEJU, W | 35 | 1 | 2 | 2 | 0 | 1 | 3 | 2 | 3 | 1 | 3 | 8 | 9 | 1.762 |
| 1714 | MATH | 122 | Precalculus | CAI, A | 25 | 5 | 3 | 4 | 1 | 1 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 3.472 |
| 1715 | MATH | 122 | Precalculus | BROGES, A | 20 | 2 | 2 | 1 | 1 | 3 | 3 | 0 | 1 | 0 | 3 | 0 | 4 | 2.819 |
| 1716 | MATH | 122 | Precalculus | LIU, Z | 23 | 3 | 1 | 0 | 3 | 1 | 4 | 2 | 0 | 4 | 0 | 1 | 4 | 2.689 |
| 1717 | MATH | 122 | Precalculus | COLON, C | 25 | 2 | 3 | 2 | 1 | 0 | 2 | 1 | 1 | 0 | 2 | 2 | 9 | 2.650 |
| 1718 | MATH | 122 | Precalculus | SPITZ, H | 30 | 3 | 3 | 2 | 1 | 1 | 1 | 2 | 1 | 5 | 2 | 2 | 7 | 2.500 |
| 1719 | MATH | 122 | Precalculus | ARCHETTI, M | 30 | 0 | 4 | 1 | 2 | 5 | 3 | 1 | 0 | 2 | 2 | 3 | 7 | 2.483 |
| 1720 | MATH | 122 | Precalculus | OZKAN, E | 33 | 0 | 3 | 3 | 3 | 0 | 0 | 1 | 1 | 3 | 2 | 2 | 14 | 2.467 |
| 1721 | MATH | 122 | Precalculus | MARKOWITZ, E | 33 | 2 | 4 | 2 | 0 | 3 | 2 | 2 | 2 | 3 | 2 | 3 | 7 | 2.460 |
| 1722 | MATH | 122 | Precalculus | FLYNN, D | 33 | 4 | 2 | 0 | 1 | 3 | 3 | 5 | 5 | 3 | 3 | 2 | 2 | 2.387 |
| 1723 | MATH | 122 | Precalculus | JOSEPH, M | 28 | 2 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 2 | 0 | 3 | 15 | 2.283 |
| 1724 | MATH | 122 | Precalculus | TOLEDO, D | 28 | 2 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 2 | 0 | 3 | 15 | 2.283 |
| 1725 | MATH | 122 | Precalculus | BEGA, J | 33 | 3 | 2 | 0 | 1 | 1 | 1 | 2 | 4 | 3 | 3 | 3 | 10 | 2.161 |
| 1726 | MATH | 122 | Precalculus | LEE, H | 18 | 2 | 0 | 1 | 0 | 0 | 0 | 2 | 4 | 3 | 3 | 0 | 3 | 2.160 |
| 1727 | MATH | 122 | Precalculus | LEE, D | 30 | 0 | 1 | 1 | 2 | 3 | 4 | 0 | 2 | 6 | 2 | 4 | 5 | 2.012 |
| 1728 | MATH | 122 | Precalculus | CLARKE, A | 19 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 3 | 10 | 1.811 |
| 1729 | MATH | 122 | Precalculus | YUABOV, D | 34 | 1 | 3 | 0 | 1 | 2 | 0 | 1 | 1 | 0 | 1 | 8 | 16 | 1.700 |
| 1730 | MATH | 122 | Precalculus | OZKAN, E | 32 | 0 | 1 | 0 | 1 | 2 | 0 | 4 | 2 | 3 | 4 | 4 | 10 | 1.695 |
| 1731 | MATH | 122 | Precalculus | TOLEDO, D | 33 | 0 | 2 | 1 | 0 | 3 | 0 | 0 | 2 | 1 | 1 | 7 | 15 | 1.612 |
| 1732 | MATH | 122 | Precalculus | BEGA, J | 33 | 0 | 0 | 1 | 2 | 1 | 0 | 1 | 3 | 2 | 5 | 5 | 13 | 1.500 |
| 1733 | MATH | 122 | Precalculus | DHARMA, J | 31 | 2 | 0 | 1 | 1 | 0 | 2 | 0 | 1 | 0 | 0 | 10 | 12 | 1.318 |
| 1734 | MATH | 122 | Precalculus | GANT, S | 29 | 1 | 0 | 1 | 0 | 2 | 0 | 1 | 3 | 1 | 0 | 10 | 10 | 1.247 |
| 1735 | MATH | 122 | Precalculus | DHARMA, J | 33 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 5 | 2 | 0 | 9 | 13 | 1.200 |
| 1736 | MATH | 122 | Precalculus | CLARKE, A | 20 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 4 | 4 | 9 | 0.900 |
| 1737 | MATH | 128 | Mathematical Design | HANUSA, C | 23 | 4 | 2 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 10 | 3.254 |
| 1738 | MATH | 131 | Calculus with Applications I | GJONLEKAJ, M | 20 | 1 | 2 | 3 | 4 | 2 | 0 | 0 | 2 | 0 | 0 | 1 | 5 | 3.087 |
| 1739 | MATH | 131 | Calculus with Applications I | GREENMAN, J | 25 | 3 | 2 | 2 | 0 | 1 | 2 | 0 | 0 | 2 | 0 | 4 | 9 | 2.450 |
| 1740 | MATH | 131 | Calculus with Applications I | GREENMAN, J | 30 | 0 | 4 | 0 | 0 | 1 | 1 | 0 | 2 | 2 | 5 | 2 | 13 | 2.006 |
| 1741 | MATH | 131 | Calculus with Applications I | GREENMAN, J | 20 | 1 | 1 | 0 | 0 | 2 | 0 | 1 | 2 | 4 | 5 | 2 | 2 | 1.783 |
| 1742 | MATH | 132 | Calc Appl Soc Sci II | GOLDMAN, S | 13 | 2 | 0 | 2 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 2 | 2.700 |
| 1743 | MATH | 141 | Calculus/Differentiation | SULTAN, Z | 15 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 4 | 3.370 |
| 1744 | MATH | 141 | Calculus/Differentiation | WEN, C | 30 | 6 | 6 | 2 | 3 | 2 | 2 | 1 | 2 | 0 | 0 | 2 | 4 | 3.192 |
| 1745 | MATH | 141 | Calculus/Differentiation | ERLBAUM, S | 30 | 1 | 5 | 1 | 4 | 3 | 2 | 0 | 0 | 2 | 3 | 1 | 7 | 2.805 |
| 1746 | MATH | 141 | Calculus/Differentiation | FLYNN, D | 30 | 2 | 4 | 2 | 2 | 6 | 2 | 1 | 4 | 1 | 2 | 1 | 3 | 2.793 |
| 1747 | MATH | 141 | Calculus/Differentiation | KALRA, P | 21 | 0 | 1 | 2 | 2 | 2 | 2 | 1 | 4 | 1 | 1 | 0 | 4 | 2.650 |
| 1748 | MATH | 141 | Calculus/Differentiation | WEN, C | 30 | 3 | 1 | 2 | 1 | 4 | 4 | 0 | 0 | 3 | 3 | 1 | 8 | 2.618 |
| 1749 | MATH | 141 | Calculus/Differentiation | LORD, A | 29 | 1 | 2 | 3 | 4 | 1 | 1 | 1 | 0 | 2 | 0 | 4 | 9 | 2.511 |
| 1750 | MATH | 141 | Calculus/Differentiation | BROGES, A | 20 | 2 | 2 | 2 | 4 | 0 | 1 | 0 | 0 | 1 | 4 | 2 | 2 | 2.500 |
| 1751 | MATH | 141 | Calculus/Differentiation | WANG, A | 30 | 3 | 2 | 2 | 2 | 3 | 0 | 1 | 2 | 1 | 3 | 4 | 7 | 2.348 |
| 1752 | MATH | 141 | Calculus/Differentiation | LI, H | 30 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 1 | 7 | 2 | 2 | 7 | 2.252 |
| 1753 | MATH | 141 | Calculus/Differentiation | KIMATOV, M | 30 | 4 | 0 | 3 | 1 | 1 | 2 | 2 | 2 | 1 | 4 | 4 | 5 | 2.213 |
| 1754 | MATH | 141 | Calculus/Differentiation | MILLER, R | 29 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 1 | 3 | 2 | 2 | 14 | 1.808 |
| 1755 | MATH | 141 | Calculus/Differentiation | LEE, D | 20 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 2 | 2 | 1 | 3 | 8 | 1.783 |
| 1756 | MATH | 141 | Calculus/Differentiation | NICASTRO, S | 29 | 2 | 1 | 1 | 0 | 2 | 0 | 3 | 3 | 2 | 4 | 6 | 5 | 1.750 |
| 1757 | MATH | 141 | Calculus/Differentiation | NEVAREZ, B | 30 | 0 | 2 | 0 | 0 | 2 | 1 | 3 | 3 | 2 | 1 | 7 | 9 | 1.619 |
| 1758 | MATH | 141 | Calculus/Differentiation | GANT, S | 30 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 1 | 3 | 5 | 3 | 12 | 1.471 |
| 1759 | MATH | 142 | Calculus/Integration | ERLBAUM, S | 35 | 1 | 6 | 4 | 3 | 0 | 5 | 0 | 1 | 3 | 1 | 2 | 9 | 2.858 |
| 1760 | MATH | 142 | Calculus/Integration | ERLBAUM, S | 35 | 2 | 6 | 2 | 2 | 3 | 7 | 1 | 2 | 1 | 1 | 3 | 5 | 2.763 |
| 1761 | MATH | 142 | Calculus/Integration | RONG, Y | 25 | 2 | 0 | 0 | 3 | 1 | 1 | 2 | 3 | 3 | 5 | 2 | 3 | 2.014 |
| 1762 | MATH | 142 | Calculus/Integration | GANGARAM, E | 30 | 1 | 0 | 0 | 0 | 7 | 2 | 2 | 5 | 1 | 2 | 5 | 5 | 1.948 |
| 1763 | MATH | 142 | Calculus/Integration | KOROVESHI, B | 28 | 0 | 1 | 1 | 0 | 1 | 2 | 4 | 6 | 4 | 2 | 5 | 2 | 1.773 |
| 1764 | MATH | 142 | Calculus/Integration | KOROVESHI, B | 30 | 1 | 1 | 0 | 1 | 1 | 0 | 3 | 3 | 8 | 0 | 7 | 5 | 1.632 |
| 1765 | MATH | 143 | Calculus-Infinite Series | ERLBAUM, S | 31 | 2 | 4 | 1 | 1 | 1 | 1 | 4 | 3 | 0 | 6 | 5 | 2 | 2.068 |
| 1766 | MATH | 143 | Calculus-Infinite Series | WILSON, S | 24 | 0 | 0 | 0 | 1 | 4 | 5 | 1 | 2 | 2 | 0 | 4 | 5 | 2.026 |
| 1767 | MATH | 143 | Calculus-Infinite Series | BRAUN, M | 28 | 1 | 3 | 0 | 3 | 0 | 0 | 4 | 2 | 1 | 4 | 9 | 1 | 1.659 |
| 1768 | MATH | 143 | Calculus-Infinite Series | ADRIAN, M | 29 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 3 | 2 | 2 | 6 | 11 | 1.594 |
| 1769 | MATH | 143 | Calculus-Infinite Series | BRAUN, M | 27 | 3 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 1 | 2 | 10 | 5 | 1.559 |
| 1770 | MATH | 143 | Calculus-Infinite Series | BERMAN, G | 20 | 0 | 1 | 1 | 0 | 1 | 1 | 2 | 1 | 2 | 0 | 9 | 1 | 1.300 |
| 1771 | MATH | 151 | Calc/Diff & Integtn | SPITZ, H | 35 | 1 | 3 | 1 | 2 | 0 | 4 | 1 | 1 | 5 | 0 | 7 | 10 | 1.996 |
| 1772 | MATH | 151 | Calc/Diff & Integtn | SABITOVA, M | 30 | 1 | 5 | 0 | 1 | 0 | 0 | 1 | 8 | 0 | 1 | 7 | 5 | 1.942 |
| 1773 | MATH | 151 | Calc/Diff & Integtn | SPITZ, H | 34 | 1 | 1 | 2 | 1 | 2 | 0 | 1 | 4 | 2 | 4 | 8 | 8 | 1.631 |
| 1774 | MATH | 152 | Calc/Integration & Infinite | ZAKERI, S | 32 | 2 | 2 | 2 | 2 | 2 | 5 | 2 | 3 | 1 | 0 | 5 | 6 | 2.377 |
| 1775 | MATH | 152 | Calc/Integration & Infinite | MILLER, D | 24 | 1 | 0 | 2 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 4 | 11 | 1.977 |
| 1776 | MATH | 152 | Calc/Integration & Infinite | NEVAREZ, B | 32 | 1 | 1 | 1 | 2 | 1 | 0 | 2 | 2 | 0 | 2 | 5 | 15 | 1.876 |
| 1777 | MATH | 152 | Calc/Integration & Infinite | MILLER, D | 25 | 1 | 1 | 1 | 0 | 0 | 1 | 2 | 0 | 3 | 1 | 6 | 9 | 1.569 |
| 1778 | MATH | 152 | Calc/Integration & Infinite | DHARMA, J | 13 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 4 | 6 | 0.957 |
| 1779 | MATH | 201 | Multivariable Calculus | MITRA, S | 21 | 1 | 3 | 0 | 3 | 1 | 6 | 5 | 0 | 0 | 0 | 0 | 0 | 2.979 |
| 1780 | MATH | 201 | Multivariable Calculus | JOSEPH, M | 30 | 4 | 2 | 3 | 0 | 4 | 2 | 0 | 2 | 4 | 0 | 2 | 7 | 2.752 |
| 1781 | MATH | 201 | Multivariable Calculus | BERMAN, G | 15 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 3 | 2 | 1 | 1.986 |
| 1782 | MATH | 202 | Advanced Calculus | SARIC, D | 13 | 1 | 3 | 0 | 3 | 0 | 2 | 0 | 1 | 0 | 0 | 2 | 1 | 2.775 |
| 1783 | MATH | 202 | Advanced Calculus | ADRIAN, M | 29 | 4 | 2 | 1 | 0 | 3 | 4 | 3 | 2 | 3 | 2 | 2 | 3 | 2.519 |
| 1784 | MATH | 205 | Mathematical Problem Solving | GANGARAM, E | 17 | 0 | 1 | 1 | 0 | 1 | 2 | 2 | 1 | 3 | 1 | 0 | 5 | 2.400 |
| 1785 | MATH | 220 | Discrete Mathematics | MILLER, D | 18 | 3 | 2 | 0 | 5 | 3 | 2 | 1 | 0 | 0 | 1 | 0 | 1 | 3.188 |
| 1786 | MATH | 223 | Diff Equa/Num Meth 1 | BRAUN, M | 20 | 1 | 2 | 2 | 0 | 2 | 3 | 0 | 0 | 3 | 2 | 5 | 0 | 2.030 |
| 1787 | MATH | 231 | Linear Algebra I | ZAKERI, S | 32 | 4 | 5 | 2 | 4 | 4 | 3 | 0 | 5 | 2 | 0 | 1 | 2 | 3.003 |
| 1788 | MATH | 231 | Linear Algebra I | JIANG, Y | 28 | 1 | 2 | 1 | 6 | 1 | 0 | 1 | 1 | 2 | 3 | 2 | 7 | 2.460 |
| 1789 | MATH | 231 | Linear Algebra I | JOSEPH, M | 30 | 1 | 2 | 2 | 2 | 2 | 2 | 0 | 4 | 3 | 0 | 3 | 9 | 2.405 |
| 1790 | MATH | 231 | Linear Algebra I | JOSEPH, M | 30 | 0 | 1 | 1 | 2 | 2 | 5 | 2 | 1 | 3 | 1 | 3 | 9 | 2.214 |
| 1791 | MATH | 231 | Linear Algebra I | JIANG, Y | 28 | 1 | 3 | 0 | 2 | 0 | 2 | 4 | 1 | 1 | 1 | 4 | 8 | 2.205 |
| 1792 | MATH | 231 | Linear Algebra I | VLAMIS, N | 30 | 1 | 1 | 0 | 0 | 1 | 2 | 0 | 7 | 5 | 0 | 3 | 10 | 1.945 |
| 1793 | MATH | 231 | Linear Algebra I | RONG, Y | 22 | 0 | 0 | 0 | 0 | 3 | 0 | 4 | 4 | 1 | 4 | 1 | 5 | 1.876 |
| 1794 | MATH | 231 | Linear Algebra I | PANDAZIS, M | 32 | 1 | 0 | 0 | 2 | 2 | 2 | 3 | 2 | 3 | 7 | 2 | 8 | 1.875 |
| 1795 | MATH | 231 | Linear Algebra I | KAHAN, S | 27 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 4 | 19 | 1.625 |
| 1796 | MATH | 231 | Linear Algebra I | KAHAN, S | 30 | 1 | 0 | 1 | 0 | 1 | 2 | 1 | 1 | 2 | 1 | 10 | 10 | 1.240 |
| 1797 | MATH | 232 | Linear Algebra II | LEE, D | 23 | 0 | 1 | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 0 | 2 | 14 | 1.856 |
| 1798 | MATH | 241 | Intro Prob & Math Stat | TERILLA, J | 25 | 1 | 14 | 0 | 0 | 5 | 3 | 0 | 1 | 0 | 0 | 0 | 1 | 3.546 |
| 1799 | MATH | 241 | Intro Prob & Math Stat | TERILLA, J | 25 | 2 | 5 | 1 | 3 | 5 | 0 | 2 | 2 | 0 | 0 | 0 | 5 | 3.260 |
| 1800 | MATH | 241 | Intro Prob & Math Stat | GANGARAM, E | 25 | 2 | 2 | 3 | 1 | 1 | 0 | 0 | 3 | 1 | 2 | 1 | 9 | 2.694 |
| 1801 | MATH | 241 | Intro Prob & Math Stat | PANDAZIS, M | 31 | 0 | 2 | 1 | 4 | 3 | 2 | 0 | 5 | 2 | 2 | 2 | 7 | 2.378 |
| 1802 | MATH | 241 | Intro Prob & Math Stat | GANGARAM, E | 25 | 1 | 1 | 4 | 0 | 1 | 1 | 1 | 3 | 0 | 0 | 4 | 9 | 2.300 |
| 1803 | MATH | 241 | Intro Prob & Math Stat | LIU, Z | 30 | 3 | 5 | 0 | 3 | 1 | 0 | 0 | 2 | 1 | 0 | 7 | 7 | 2.300 |
| 1804 | MATH | 241 | Intro Prob & Math Stat | WANG, A | 30 | 3 | 2 | 2 | 3 | 0 | 0 | 2 | 3 | 1 | 1 | 9 | 4 | 1.946 |
| 1805 | MATH | 241 | Intro Prob & Math Stat | WANG, A | 24 | 2 | 1 | 0 | 1 | 2 | 0 | 0 | 3 | 1 | 0 | 6 | 8 | 1.813 |
| 1806 | MATH | 241 | Intro Prob & Math Stat | KOROVESHI, B | 26 | 0 | 0 | 1 | 0 | 1 | 0 | 2 | 2 | 6 | 0 | 5 | 9 | 1.500 |
| 1807 | MATH | 241 | Intro Prob & Math Stat | KOROVESHI, B | 26 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 1 | 6 | 0 | 5 | 11 | 1.367 |
| 1808 | MATH | 242 | Methods Of Math Statistics | SISSER, F | 30 | 1 | 2 | 3 | 4 | 4 | 6 | 0 | 1 | 3 | 0 | 3 | 3 | 2.652 |
| 1809 | MATH | 242 | Methods Of Math Statistics | SISSER, F | 30 | 3 | 2 | 0 | 2 | 6 | 2 | 2 | 3 | 3 | 0 | 4 | 3 | 2.433 |
| 1810 | MATH | 245 | Mathematical Models | OVCHINNIKOV, A | 23 | 2 | 6 | 3 | 5 | 1 | 3 | 1 | 0 | 0 | 0 | 1 | 1 | 3.318 |
| 1811 | MATH | 247 | Lin Prog & Game Thy | SISSER, F | 30 | 2 | 9 | 1 | 3 | 3 | 2 | 1 | 1 | 1 | 0 | 1 | 6 | 3.250 |
| 1812 | MATH | 250 | Mathematical Computing | HANUSA, C | 22 | 4 | 6 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | 3.757 |
| 1813 | MATH | 301 | Abstract Algebra I | WILSON, S | 12 | 0 | 2 | 0 | 2 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 2 | 2.630 |
| 1814 | MATH | 301 | Abstract Algebra I | VLAMIS, N | 21 | 0 | 1 | 0 | 0 | 1 | 1 | 2 | 4 | 4 | 0 | 2 | 6 | 1.940 |
| 1815 | MATH | 305 | Number Theory | SABITOVA, M | 18 | 1 | 1 | 0 | 0 | 6 | 0 | 0 | 1 | 0 | 2 | 1 | 6 | 2.500 |
| 1816 | MATH | 318 | Foundations of Geometry | KLOSIN, K | 19 | 4 | 4 | 0 | 3 | 1 | 3 | 1 | 1 | 0 | 0 | 0 | 2 | 3.371 |
| 1817 | MATH | 320 | Point-Set Topology | KLOSIN, K | 25 | 2 | 4 | 1 | 1 | 2 | 1 | 2 | 1 | 0 | 1 | 1 | 8 | 2.956 |
| 1818 | MATH | 524 | History of Mathematics | LORD, A | 15 | 2 | 5 | 2 | 2 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 3.571 |
| 1819 | MATH | 605 | Number Theory | SABITOVA, M | 10 | 0 | 4 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 3.375 |
| 1820 | MATH | 609 | Axiomatic Set Theory | MITRA, S | 20 | 1 | 1 | 4 | 9 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 3.400 |
| 1821 | MATH | 618 | Foundations of Geometry. | KLOSIN, K | 16 | 0 | 4 | 0 | 3 | 1 | 1 | 2 | 1 | 1 | 0 | 1 | 2 | 2.850 |
| 1822 | MATH | 625 | Numerical Analysis II | OVCHINNIKOV, A | 11 | 4 | 5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 3.518 |
| 1823 | MATH | 114W | Elementary Prob&Stat | CAI, A | 30 | 6 | 0 | 3 | 5 | 4 | 2 | 2 | 1 | 1 | 0 | 1 | 5 | 3.092 |
| 1824 | MATH | 114W | Elementary Prob&Stat | RICCARDO, M | 20 | 2 | 3 | 1 | 4 | 3 | 0 | 0 | 3 | 1 | 1 | 1 | 1 | 2.874 |
| 1825 | MATH | 385W | Math Found Sec Educ | ARTZT, A | 12 | 0 | 1 | 4 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3.417 |
| 1826 | MATH | 385W | Math Found Sec Educ | MARKINSON, M | 10 | 0 | 0 | 1 | 2 | 3 | 0 | 2 | 0 | 1 | 0 | 0 | 1 | 2.844 |
CourseNumberPattern = r'^[1-3][0-9]{2}W?$'
math_df = math_df[math_df['NBR'].str.contains(CourseNumberPattern)]
math_df_results = calculate_average_gpas(math_df)
math_df_results
Average GPA for this entire subject in Spring 2023 is: 2.3, which is a C+ The hardest class based on average GPA in Spring 2023 is class number: 143 with an average GPA of 1.7, which is a C- Standard deviation tells us about the spread of the grades that students received in each class. A higher standard deviation indicates a wider range of grades, while a lower standard deviation indicates that grades were more closely clustered around the average.
| CLASS NUMBER | PROF | AVG GPA PROF | AVG GPA PROF LETTER | STD DEV GPA PROF | |
|---|---|---|---|---|---|
| 0 | 110 | ALI, M | 3.5 | B+ | 0.69 |
| 1 | 110 | LASKER, M | 2.9 | B- | 1.17 |
| 2 | 110 | GILLMAN, P | 1.3 | D | 1.31 |
| 3 | 110 | All Professors | 2.5 | C+ | 1.48 |
| 4 | 114 | CHOW, J | 3.0 | B | 0.98 |
| 5 | 115 | ALI, M | 3.4 | B+ | 0.90 |
| 6 | 115 | UDDIN, J | 2.8 | B- | 1.35 |
| 7 | 115 | NAKAYAMA, A | 2.6 | C+ | 1.44 |
| 8 | 115 | JOANIDHI, Z | 2.6 | C+ | 1.29 |
| 9 | 115 | OSTROWSKY, A | 2.5 | C+ | 1.37 |
| 10 | 115 | TOBAR, J | 1.9 | C- | 1.15 |
| 11 | 115 | XU, R | 2.1 | C | 1.22 |
| 12 | 115 | LEHMAN, S | 1.8 | C- | 1.21 |
| 13 | 115 | KALRA, P | 2.0 | C | 1.36 |
| 14 | 115 | BERGER, K | 1.8 | C- | 1.53 |
| 15 | 115 | BOTEJU, W | 1.5 | D | 1.47 |
| 16 | 115 | PORCHETTA, E | 1.5 | D | 1.52 |
| 17 | 115 | WOLF-SONKIN, V | 1.0 | D | 1.36 |
| 18 | 115 | SHEN, T | 1.1 | D | 1.24 |
| 19 | 115 | All Professors | 1.9 | C- | 1.48 |
| 20 | 119 | GREENMAN, J | 3.2 | B | 1.01 |
| 21 | 119 | RICCARDO, M | 3.1 | B | 1.09 |
| 22 | 119 | KLOSIN, K | 2.9 | B- | 1.18 |
| 23 | 119 | CHOW, J | 2.7 | B- | 1.32 |
| 24 | 119 | BERGER, K | 2.7 | B- | 1.26 |
| 25 | 119 | LIN, Y | 2.6 | C+ | 1.31 |
| 26 | 119 | LI, H | 1.9 | C- | 1.40 |
| 27 | 119 | All Professors | 2.7 | B- | 1.29 |
| 28 | 120 | TERILLA, J | 3.1 | B | 1.02 |
| 29 | 120 | GOLDMAN, S | 3.0 | B | 0.92 |
| 30 | 120 | GONZALEZ, R | 2.3 | C+ | 1.21 |
| 31 | 120 | LEE, H | 2.6 | C+ | 0.81 |
| 32 | 120 | LEE, D | 2.5 | C+ | 1.07 |
| 33 | 120 | TOBAR, J | 2.5 | C+ | 1.40 |
| 34 | 120 | BERGER, K | 2.4 | C+ | 1.24 |
| 35 | 120 | SABITOVA, M | 2.0 | C | 1.25 |
| 36 | 120 | SHEN, T | 1.8 | C- | 1.17 |
| 37 | 120 | BOTEJU, W | 1.8 | C- | 1.44 |
| 38 | 120 | All Professors | 2.4 | C+ | 1.24 |
| 39 | 122 | CAI, A | 3.5 | B+ | 0.68 |
| 40 | 122 | BROGES, A | 2.8 | B- | 1.04 |
| 41 | 122 | LIU, Z | 2.7 | B- | 1.01 |
| 42 | 122 | COLON, C | 2.6 | C+ | 1.41 |
| 43 | 122 | SPITZ, H | 2.5 | C+ | 1.28 |
| 44 | 122 | ARCHETTI, M | 2.5 | C+ | 1.28 |
| 45 | 122 | OZKAN, E | 2.1 | C | 1.28 |
| 46 | 122 | MARKOWITZ, E | 2.5 | C+ | 1.31 |
| 47 | 122 | FLYNN, D | 2.4 | C+ | 1.10 |
| 48 | 122 | JOSEPH, M | 2.3 | C+ | 1.55 |
| 49 | 122 | TOLEDO, D | 1.9 | C- | 1.58 |
| 50 | 122 | BEGA, J | 1.8 | C- | 1.28 |
| 51 | 122 | LEE, H | 2.2 | C | 0.96 |
| 52 | 122 | LEE, D | 2.0 | C | 1.16 |
| 53 | 122 | CLARKE, A | 1.4 | D | 1.34 |
| 54 | 122 | YUABOV, D | 1.7 | C- | 1.69 |
| 55 | 122 | DHARMA, J | 1.3 | D | 1.44 |
| 56 | 122 | GANT, S | 1.2 | D | 1.42 |
| 57 | 122 | All Professors | 2.1 | C | 1.40 |
| 58 | 128 | HANUSA, C | 3.3 | B+ | 1.07 |
| 59 | 131 | GJONLEKAJ, M | 3.1 | B | 1.02 |
| 60 | 131 | GREENMAN, J | 2.1 | C | 1.39 |
| 61 | 131 | All Professors | 2.3 | C+ | 1.38 |
| 62 | 132 | GOLDMAN, S | 2.7 | B- | 1.29 |
| 63 | 141 | SULTAN, Z | 3.4 | B+ | 0.62 |
| 64 | 141 | WEN, C | 2.9 | B- | 1.17 |
| 65 | 141 | ERLBAUM, S | 2.8 | B- | 1.18 |
| 66 | 141 | FLYNN, D | 2.8 | B- | 1.04 |
| 67 | 141 | KALRA, P | 2.6 | C+ | 0.81 |
| 68 | 141 | LORD, A | 2.5 | C+ | 1.46 |
| 69 | 141 | BROGES, A | 2.5 | C+ | 1.43 |
| 70 | 141 | WANG, A | 2.3 | C+ | 1.45 |
| 71 | 141 | LI, H | 2.3 | C+ | 1.16 |
| 72 | 141 | KIMATOV, M | 2.2 | C | 1.41 |
| 73 | 141 | MILLER, R | 1.8 | C- | 1.03 |
| 74 | 141 | LEE, D | 1.8 | C- | 1.28 |
| 75 | 141 | NICASTRO, S | 1.8 | C- | 1.35 |
| 76 | 141 | NEVAREZ, B | 1.6 | D | 1.33 |
| 77 | 141 | GANT, S | 1.5 | D | 0.94 |
| 78 | 141 | All Professors | 2.4 | C+ | 1.32 |
| 79 | 142 | ERLBAUM, S | 2.8 | B- | 1.20 |
| 80 | 142 | RONG, Y | 2.0 | C | 1.13 |
| 81 | 142 | GANGARAM, E | 1.9 | C- | 1.17 |
| 82 | 142 | KOROVESHI, B | 1.7 | C- | 1.14 |
| 83 | 142 | All Professors | 2.2 | C | 1.26 |
| 84 | 143 | ERLBAUM, S | 2.1 | C | 1.42 |
| 85 | 143 | WILSON, S | 2.0 | C | 1.13 |
| 86 | 143 | BRAUN, M | 1.6 | D | 1.54 |
| 87 | 143 | ADRIAN, M | 1.6 | D | 1.42 |
| 88 | 143 | BERMAN, G | 1.3 | D | 1.41 |
| 89 | 143 | All Professors | 1.7 | C- | 1.45 |
| 90 | 151 | SPITZ, H | 1.8 | C- | 1.43 |
| 91 | 151 | SABITOVA, M | 1.9 | C- | 1.51 |
| 92 | 151 | All Professors | 1.9 | C- | 1.46 |
| 93 | 152 | ZAKERI, S | 2.4 | C+ | 1.33 |
| 94 | 152 | MILLER, D | 1.8 | C- | 1.50 |
| 95 | 152 | NEVAREZ, B | 1.9 | C- | 1.48 |
| 96 | 152 | DHARMA, J | 1.0 | D | 1.21 |
| 97 | 152 | All Professors | 1.8 | C- | 1.47 |
| 98 | 201 | MITRA, S | 3.0 | B | 0.62 |
| 99 | 201 | JOSEPH, M | 2.8 | B- | 1.20 |
| 100 | 201 | BERMAN, G | 2.0 | C | 1.40 |
| 101 | 201 | All Professors | 2.6 | C+ | 1.17 |
| 102 | 202 | SARIC, D | 2.8 | B- | 1.38 |
| 103 | 202 | ADRIAN, M | 2.5 | C+ | 1.17 |
| 104 | 202 | All Professors | 2.6 | C+ | 1.24 |
| 105 | 205 | GANGARAM, E | 2.4 | C+ | 0.84 |
| 106 | 220 | MILLER, D | 3.2 | B | 0.75 |
| 107 | 223 | BRAUN, M | 2.0 | C | 1.47 |
| 108 | 231 | ZAKERI, S | 3.0 | B | 0.97 |
| 109 | 231 | JIANG, Y | 2.3 | C+ | 1.35 |
| 110 | 231 | JOSEPH, M | 2.3 | C+ | 1.20 |
| 111 | 231 | VLAMIS, N | 1.9 | C- | 1.06 |
| 112 | 231 | RONG, Y | 1.9 | C- | 0.81 |
| 113 | 231 | PANDAZIS, M | 1.9 | C- | 1.03 |
| 114 | 231 | KAHAN, S | 1.4 | D | 1.48 |
| 115 | 231 | All Professors | 2.1 | C | 1.28 |
| 116 | 232 | LEE, D | 1.9 | C- | 1.19 |
| 117 | 241 | TERILLA, J | 3.4 | B+ | 0.66 |
| 118 | 241 | GANGARAM, E | 2.5 | C+ | 1.40 |
| 119 | 241 | PANDAZIS, M | 2.4 | C+ | 1.11 |
| 120 | 241 | LIU, Z | 2.3 | C+ | 1.71 |
| 121 | 241 | WANG, A | 1.9 | C- | 1.60 |
| 122 | 241 | KOROVESHI, B | 1.4 | D | 1.09 |
| 123 | 241 | All Professors | 2.3 | C+ | 1.44 |
| 124 | 242 | SISSER, F | 2.5 | C+ | 1.20 |
| 125 | 245 | OVCHINNIKOV, A | 3.3 | B+ | 0.89 |
| 126 | 247 | SISSER, F | 3.2 | B | 0.97 |
| 127 | 250 | HANUSA, C | 3.8 | A- | 0.47 |
| 128 | 301 | WILSON, S | 2.6 | C+ | 1.14 |
| 129 | 301 | VLAMIS, N | 1.9 | C- | 0.97 |
| 130 | 301 | All Professors | 2.3 | C+ | 1.09 |
| 131 | 305 | SABITOVA, M | 2.5 | C+ | 1.19 |
| 132 | 318 | KLOSIN, K | 3.4 | B+ | 0.67 |
| 133 | 320 | KLOSIN, K | 3.0 | B | 1.16 |
| 134 | 114W | CAI, A | 3.1 | B | 0.91 |
| 135 | 114W | RICCARDO, M | 2.9 | B- | 1.11 |
| 136 | 114W | All Professors | 3.0 | B | 1.00 |
| 137 | 385W | ARTZT, A | 3.4 | B+ | 0.32 |
| 138 | 385W | MARKINSON, M | 2.8 | B- | 0.59 |
| 139 | 385W | All Professors | 3.1 | B | 0.54 |
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure()
all_prof_df_results = math_df_results[math_df_results["PROF"] == 'All Professors']
sns.histplot(all_prof_df_results["STD DEV GPA PROF"], kde=True, color='skyblue')
plt.axvline(all_prof_df_results["STD DEV GPA PROF"].mean(), color='red', linestyle='dashed', linewidth=1, label='Mean')
min_ylim, max_ylim = plt.ylim()
plt.text(all_prof_df_results["STD DEV GPA PROF"].mean()*1.12, max_ylim*0.9, 'Mean: {:.2f}'.format(all_prof_df_results["STD DEV GPA PROF"].mean()))
# Generate a color palette with as many colors as classes
colors = sns.color_palette("husl", len(all_prof_df_results))
for idx, (_, row) in enumerate(all_prof_df_results.iterrows()):
plt.axvline(row["STD DEV GPA PROF"], color=colors[idx], linestyle='dotted', linewidth=0.5, label=row["CLASS NUMBER"])
plt.title("Distribution of Standard Deviations of GPAs")
plt.xlabel("Standard Deviation")
plt.ylabel("Frequency")
plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1)) # Adjusted the location to ensure it doesn't overlap with the plot
plt.show()
teacher_math_df_results = calculate_teacher_gpas(math_df)
teacher_math_df_results
Professors who teach more than one class, have a GPA that is the same or higher than the subject average GPA (2.3), and have a withdrawal percentage that is less or equal than the subject average withdrawal percentage (24.78%):
PROF AVG GPA PROF AVG GPA PROF LETTER STD DEV GPA PROF \
1 ALI, M 3.5 B+ 0.8
5 BERGER, K 2.3 C+ 1.4
9 BROGES, A 2.7 B- 1.3
10 CAI, A 3.3 B+ 0.8
11 CHOW, J 2.8 B- 1.3
15 ERLBAUM, S 2.6 C+ 1.3
16 FLYNN, D 2.6 C+ 1.1
21 GOLDMAN, S 2.9 B- 1.0
29 KALRA, P 2.3 C+ 1.1
31 KLOSIN, K 3.1 B 1.1
35 LEE, H 2.4 C+ 0.9
39 LIU, Z 2.5 C+ 1.4
54 RICCARDO, M 3.0 B 1.1
59 SISSER, F 2.8 B- 1.2
62 TERILLA, J 3.3 B+ 0.8
68 WEN, C 2.9 B- 1.2
69 WILSON, S 2.3 C+ 1.2
73 ZAKERI, S 2.7 B- 1.2
NUM OF CLASSES WITHDRAW PERCENTAGE
1 3 16.5
5 3 22.7
9 2 15.0
10 2 9.1
11 3 12.7
15 4 17.6
16 2 7.9
21 2 12.2
29 2 18.9
31 3 14.1
35 2 11.6
39 2 20.8
54 2 10.0
59 3 13.3
62 3 10.7
68 2 20.0
69 2 19.4
73 2 12.5
Note: This ignores rate my professor ratings. This is soley based on GPA and it doesn't consider how much students actually learn from these teachers!
Average standard deviation of GPA for all teachers in this subject in Spring 2023 is: 1.4
| PROF | AVG GPA PROF | AVG GPA PROF LETTER | STD DEV GPA PROF | NUM OF CLASSES | WITHDRAW PERCENTAGE | |
|---|---|---|---|---|---|---|
| 0 | ADRIAN, M | 2.1 | C | 1.4 | 2 | 24.1 |
| 1 | ALI, M | 3.5 | B+ | 0.8 | 3 | 16.5 |
| 2 | ARCHETTI, M | 2.5 | C+ | 1.3 | 1 | 23.3 |
| 3 | ARTZT, A | 3.4 | B+ | 0.3 | 1 | 0.0 |
| 4 | BEGA, J | 1.8 | C- | 1.3 | 2 | 34.8 |
| 5 | BERGER, K | 2.3 | C+ | 1.4 | 3 | 22.7 |
| 6 | BERMAN, G | 1.6 | D | 1.4 | 2 | 5.7 |
| 7 | BOTEJU, W | 1.6 | D | 1.5 | 3 | 24.8 |
| 8 | BRAUN, M | 1.7 | C- | 1.5 | 3 | 8.0 |
| 9 | BROGES, A | 2.7 | B- | 1.3 | 2 | 15.0 |
| 10 | CAI, A | 3.3 | B+ | 0.8 | 2 | 9.1 |
| 11 | CHOW, J | 2.8 | B- | 1.3 | 3 | 12.7 |
| 12 | CLARKE, A | 1.4 | D | 1.3 | 2 | 48.7 |
| 13 | COLON, C | 2.6 | C+ | 1.4 | 1 | 36.0 |
| 14 | DHARMA, J | 1.2 | D | 1.4 | 3 | 40.3 |
| 15 | ERLBAUM, S | 2.6 | C+ | 1.3 | 4 | 17.6 |
| 16 | FLYNN, D | 2.6 | C+ | 1.1 | 2 | 7.9 |
| 17 | GANGARAM, E | 2.3 | C+ | 1.3 | 4 | 28.9 |
| 18 | GANT, S | 1.4 | D | 1.2 | 2 | 37.3 |
| 19 | GILLMAN, P | 1.3 | D | 1.3 | 2 | 27.5 |
| 20 | GJONLEKAJ, M | 3.1 | B | 1.0 | 1 | 25.0 |
| 21 | GOLDMAN, S | 2.9 | B- | 1.0 | 2 | 12.2 |
| 22 | GONZALEZ, R | 2.3 | C+ | 1.2 | 3 | 29.5 |
| 23 | GREENMAN, J | 2.3 | C+ | 1.4 | 4 | 26.0 |
| 24 | HANUSA, C | 3.5 | B+ | 0.9 | 2 | 37.8 |
| 25 | JIANG, Y | 2.3 | C+ | 1.3 | 2 | 26.8 |
| 26 | JOANIDHI, Z | 2.6 | C+ | 1.3 | 1 | 25.0 |
| 27 | JOSEPH, M | 2.4 | C+ | 1.3 | 4 | 33.9 |
| 28 | KAHAN, S | 1.4 | D | 1.5 | 2 | 50.9 |
| 29 | KALRA, P | 2.3 | C+ | 1.1 | 2 | 18.9 |
| 30 | KIMATOV, M | 2.2 | C | 1.4 | 1 | 16.7 |
| 31 | KLOSIN, K | 3.1 | B | 1.1 | 3 | 14.1 |
| 32 | KOROVESHI, B | 1.6 | D | 1.1 | 4 | 24.5 |
| 33 | LASKER, M | 2.9 | B- | 1.2 | 1 | 12.5 |
| 34 | LEE, D | 2.0 | C | 1.2 | 4 | 33.0 |
| 35 | LEE, H | 2.4 | C+ | 0.9 | 2 | 11.6 |
| 36 | LEHMAN, S | 1.8 | C- | 1.2 | 2 | 25.7 |
| 37 | LI, H | 2.1 | C | 1.3 | 2 | 23.3 |
| 38 | LIN, Y | 2.6 | C+ | 1.3 | 1 | 18.2 |
| 39 | LIU, Z | 2.5 | C+ | 1.4 | 2 | 20.8 |
| 40 | LORD, A | 2.5 | C+ | 1.5 | 1 | 31.0 |
| 41 | MARKINSON, M | 2.8 | B- | 0.6 | 1 | 10.0 |
| 42 | MARKOWITZ, E | 2.5 | C+ | 1.3 | 1 | 21.2 |
| 43 | MILLER, D | 2.2 | C | 1.5 | 3 | 31.3 |
| 44 | MILLER, R | 1.8 | C- | 1.0 | 1 | 48.3 |
| 45 | MITRA, S | 3.0 | B | 0.6 | 1 | 0.0 |
| 46 | NAKAYAMA, A | 2.6 | C+ | 1.4 | 1 | 17.1 |
| 47 | NEVAREZ, B | 1.7 | C- | 1.4 | 2 | 38.7 |
| 48 | NICASTRO, S | 1.8 | C- | 1.4 | 1 | 17.2 |
| 49 | OSTROWSKY, A | 2.5 | C+ | 1.4 | 1 | 29.6 |
| 50 | OVCHINNIKOV, A | 3.3 | B+ | 0.9 | 1 | 4.3 |
| 51 | OZKAN, E | 2.1 | C | 1.3 | 2 | 36.9 |
| 52 | PANDAZIS, M | 2.1 | C | 1.1 | 2 | 23.8 |
| 53 | PORCHETTA, E | 1.5 | D | 1.5 | 1 | 30.0 |
| 54 | RICCARDO, M | 3.0 | B | 1.1 | 2 | 10.0 |
| 55 | RONG, Y | 1.9 | C- | 1.0 | 2 | 17.0 |
| 56 | SABITOVA, M | 2.1 | C | 1.4 | 3 | 28.6 |
| 57 | SARIC, D | 2.8 | B- | 1.4 | 1 | 7.7 |
| 58 | SHEN, T | 1.5 | D | 1.2 | 2 | 26.8 |
| 59 | SISSER, F | 2.8 | B- | 1.2 | 3 | 13.3 |
| 60 | SPITZ, H | 2.0 | C | 1.4 | 3 | 25.3 |
| 61 | SULTAN, Z | 3.4 | B+ | 0.6 | 1 | 26.7 |
| 62 | TERILLA, J | 3.3 | B+ | 0.8 | 3 | 10.7 |
| 63 | TOBAR, J | 2.1 | C | 1.3 | 3 | 37.2 |
| 64 | TOLEDO, D | 1.9 | C- | 1.6 | 2 | 49.2 |
| 65 | UDDIN, J | 2.8 | B- | 1.4 | 1 | 8.7 |
| 66 | VLAMIS, N | 1.9 | C- | 1.0 | 2 | 31.4 |
| 67 | WANG, A | 2.0 | C | 1.6 | 3 | 22.6 |
| 68 | WEN, C | 2.9 | B- | 1.2 | 2 | 20.0 |
| 69 | WILSON, S | 2.3 | C+ | 1.2 | 2 | 19.4 |
| 70 | WOLF-SONKIN, V | 1.0 | D | 1.4 | 2 | 47.4 |
| 71 | XU, R | 2.1 | C | 1.2 | 1 | 5.0 |
| 72 | YUABOV, D | 1.7 | C- | 1.7 | 1 | 47.1 |
| 73 | ZAKERI, S | 2.7 | B- | 1.2 | 2 | 12.5 |
import plotly.express as px
# Set the overall average GPA
mean_gpa = 2.3
# Create the scatter plot using Plotly Express
fig = px.scatter(teacher_math_df_results,
x=list(range(len(teacher_math_df_results))),
y='AVG GPA PROF',
hover_name='PROF', # This will show the professor's name when hovering over a point
title="Math Professor GPA Averages vs Math Subject Average GPA",
labels={'x': 'Professor (By Index Above)', 'y': 'Average GPA'},
size_max=100)
# Add a line for the average GPA
fig.add_shape(
type='line',
line=dict(dash='dash', color='red'),
x0=0,
x1=len(teacher_math_df_results),
y0=mean_gpa,
y1=mean_gpa,
)
# Show the plot
fig.show()
# Count the number of classes with an average GPA at or greater than 3.0 and those less than 3.0
green_percentage = teacher_math_df_results[teacher_math_df_results['AVG GPA PROF'] >= 3.0].shape[0]
red_percentage = teacher_math_df_results[teacher_math_df_results['AVG GPA PROF'] < 3.0].shape[0]
# Create the values and labels for the pie chart
values = [green_percentage, red_percentage]
labels = ['At or above 3.0 (B or above)', 'At or below 2.7 (B- or below)']
# Define the colors for each section (green and red)
colors = ['#77dd77', '#ff6961']
# Plot the pie chart
plt.figure(figsize = (6, 6))
plt.pie(values, labels = labels, colors = colors, autopct = '%1.1f%%')
# Set the title
plt.title("Average GPA of all MATH Teachers")
# Show the plot
plt.show()
MATH 110 (Mathematical Literacy: An Introduction to College Mathematics) - Mathematical literacy is necessary for success in today’s highly technological society. Students will gain hands-on experience in solving real world problems in such diverse areas as law, medicine, and politics. Applications include analysis of election results and voting schemes, interpretation of medical data, and study of the nature of fair political representation. Mathematical topics covered will include an introduction to probability and statistics through normal curves and confidence intervals; exponential and logistic growth models; and the algebraic skills necessary for all the applications covered. Extensive use will also be made of today’s sophisticated graphing calculators. Successful completion of the course satisfies the Basic Skills Requirement in Mathematics and prepares students for MATH 113, 114, 116, and 119. Not open to students who are taking or have received credit, including transfer credit or advanced placement credit, for any precalculus or calculus course
Professors:
General Average GPA for Math 110:
MATH 114 (Elementary Prob and Stats) - An introduction to mathematical probability and statistics for the general student. Not open to mathematics, physics, or chemistry majors, or to students receiving credit for MATH 114W, 241, 611, 621, or 633
Professor:
MATH 114W (Elementary Probability and Statistics) - An introduction to mathematical probability and statistics for the general student with a writing-intensive component. Includes the material in MATH 114, as well as additional topics such as sampling methods, research design, and composing and conducting surveys, explored through student research and writing assignments. Not open to mathematics, physics, or chemistry majors, or to students who are taking or have passed MATH 114, 241, 611, 621, 633, BIOL 230, ECON 249, PSYCH 107, SOC 205, 206, 207. Not open to students who will be receiving transfer credit or advanced placement credit for MATH 114
Professors:
General Average GPA for MATH 114W:
MATH 115 (College Algebra for Precalculus) - Topics include linear, polynomial, rational, and radical expressions as mathematical models; solving equations and systems of equations that arise through the application of these models. Not open to students who are taking or have received credit, including transfer credit or advanced placement credit, for any precalculus or calculus course. Students who fail or withdraw from this course multiple times may be prohibited from majoring in the sciences or mathematics; see the bulletin language for your major
Professors:
General Average GPA for MATH 115:
MATH 119 (Math for Elementary School Teachers) - This course is designed to make prospective elementary schoolteachers aware of the beauty, meaning, and relevance of mathematics. Topics are taken from those areas of mathematics that are related to the elementary school curriculum, and emphasis is placed on clearing up common misunderstandings of mathematical concepts and results
Professors:
General Average GPA for MATH 119:
MATH 120 (Discrete Math for Computer Science) - This course provides fluency in foundational mathematical concepts that appear in future courses in computer science. This course is intended for computer science majors; it does not count toward a major or minor in mathematics. Topics include sets, basic combinatorics, functions, sequences, series, products, logarithms, divisibility, and modular arithmetic. Not open to students who are taking or who have received credit for CSCI 120 or MATH 220
Professors:
General Average GPA for MATH 120:
MATH 122 (Precalculus) - This course offers a thorough introduction to the topics required for calculus. Topics include real and complex numbers, algebra of functions, the fundamental theorem of algebra, trigonometry, logarithms and exponential functions, conic sections, and the use of graphic calculators. Students unsure of their preparation for calculus are advised to take the Queens College mathematics placement test
Professors:
General Average GPA for MATH 122:
MATH 128 (Mathematical Design) - Students will program computers to create digital art based on mathematical exploration of twodimensional geometry. Topics include transformations of the plane, trigonometric functions, polar coordinates, parametric functions, and Mobius transformations. No prior experience in programming is necessary
Professor:
MATH 131 (Calculus with Applications to Social Sciences 1) - Introduction of the fundamental ideas and techniques of calculus to nonscience students. Special emphasis is given to applications. Topics include functions and graphs; derivatives and differentiation techniques; the marginal concept in economics; optimization methods; compound interest; exponential and logarithmic functions. Not open to students who are taking any other calculus course or have received credit, including transfer credit or advanced placement credit, forany calculus course. Students who fail or withdraw from this course multiple times may be prohibited from majoring in the sciences or mathematics; see the bulletin language for your major. Fall, Spring
Professors:
General Average GPA for MATH 131:
MATH 132 (Calculus with Applications to Social Sciences 2) - A continuation of MATH 131. Topics include limits and continuity; mean value theorem; antiderivatives; integrals and integration techniques; applications of the definite integral; the calculus of logarithmic, exponential, and trigonometric functions. This course prepares students who have taken MATH 131 to continue into MATH 143. Students who fail or withdraw from this course multiple times may be prohibited from majoring in the sciences or mathematics; seethe bulletin language for your major
Professor:
MATH 141 (Calculus-Differentiation) - The first part of a three-semester sequence (MATH 141, 142, 143) covering the same material as MATH 151 and 152. Credit is given for each course satisfactorily completed; a student need not take the entire sequence. Not open to students who are taking any other calculus course or have received credit, including transfer credit or advanced placement credit, for any calculus course. Students who failor withdraw from this course multiple times may be prohibited from majoring in the sciences or mathematics; see the bulletin language for your major. Fall, Spring (MQR)
Professors:
General Average GPA for MATH 141:
MATH 142 (Calculus-Integration) - A continuation of MATH 141. Not open to students who are taking any other calculus course or have received credit, including transfer credit or advanced placement credit, for any calculus course other than MATH 141 or MATH 151.Students who fail or withdraw from this course multiple times may be prohibited from majoring in the sciences or mathematics; see the bulletin language for your major. Fall, Spring (MQR)
Professors:
General Average GPA for MATH 142:
MATH 143 (Calculus-Integration) - A continuation of MATH 142. Not open to students who are taking any other calculus course or have received credit, including transfer credit or advanced placement credit, for any calculus course other than MATH 131, MATH 132, MATH141, MATH 142 or MATH 151. Students who fail or withdraw from this course multiple times may be prohibited from majoring in the sciences or mathematics; see the bulletin language for your major. Fall, Spring (MQR)
Professors:
General Average GPA for MATH 142: